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69 protocols using amos 21

1

Confirmatory Factor Analysis of Psychological Scales

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For PSI-II, confirmatory factor analysis was conducted with IBM AMOS 21, constraining the instruments into three dimensions stated in the original instrument. The indices recommended by Schreiber et al. [29 (link)] to indicate an adequate model fit were goodness-of-fit (GFI), comparative fit index (CFI), root mean square error of approximation (RMSEA), and they yielded acceptable scores (GFI = 0.95, CFI = 0.97, TLI = 0.92, and RMSEA = 0.056).
For the Tromsø social intelligence scale, confirmatory factor analysis was conducted with IBM AMOS 21, constraining the instruments into three dimensions stated in the original instrument. The indices recommended by Schreiber et al. [29 (link)] to indicate an adequate model fit were goodness-of-fit (GFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA), and they yielded acceptable scores (GFI = 0.97, CFI = 0.99, TLI = 0.95, and RMSEA = 0.044).
For the IAT, a confirmatory factor analysis was conducted with IBM AMOS 21, constraining the instruments into six dimensions stated by the authors. The indices recommended by Schreiber et al. [29 (link)] to indicate an adequate model fit were goodness-of-fit (GFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA), and they yielded acceptable scores (GFI = 0.93, CFI = 0.95, TLI = 0.93, and RMSEA = 0.067).
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2

Transformational Leadership and Organizational Empowerment

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The collected data were analyzed using SPSS WIN 21.0 (IBM Corp., Armonk, NY, USA) and IBM AMOS 21.0. First, general characteristics of the participants were analyzed through descriptive statistics such as frequency and percentage. Second, the transformational leadership, organizational empowerment, and social capital of the participants were analyzed through average and standard deviation. Third, the relationship between transformational leadership, organizational empowerment, and social capital was analyzed using Pearson correlation coefficient. Finally, IBM AMOS 21.0 was used to examine the mediating effect of social capital and fit of the study model in relation to transformational leadership and organizational empowerment of the participants. The fit of the model was based on the TuckereLewis index, comparative fit index, goodness-of-fit index, incremental fit index, normal fit index, root-mean-square residual, and root-meansquare error of approximation. The Sobel test for the assessment of validity and accuracy was implemented to validate the mediating effect.
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3

Healthy Lifestyle Determinants: Autonomy and Self-Control

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The data were analyzed using AMOS 21.0 and SPSS 26.0 statistical programs. First, descriptive statistical analyses and correlational analyses were performed to identify the general trends and characteristics of the data (such as mean and standard deviation). Next, factor analyses were performed to verify the construct validity of the surveys, and reliability analysis was performed for each factor (Cronbach’s α). Furthermore, to measure changes in the participants’ healthy lifestyle, a paired t-test was performed. To test whether autonomy in exercise participation and self-control had a significant impact on posttest healthy lifestyle (T2) even after controlling for the impact of pretest healthy lifestyle (T1) on posttest healthy lifestyle, hierarchical regression analyses were performed.
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4

Examining Psychological Distress Impacts

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We applied path analysis and structural equation modeling to estimate the relationships among PTSS, anxiety, depression, and illness intrusiveness.
First, we estimated the direct effects between PTSS and anxiety, PTSS and depression, PTSS and illness intrusiveness, anxiety and illness intrusiveness, and depression and illness intrusiveness. Then, we computed the overall indirect effects of PTSS on illness intrusiveness through anxiety and depression using a bootstrap of 10,000 samples. We used the Sobel test to calculate the indirect effects of PTSS on illness intrusiveness separately through anxiety, through depression, and through depression to anxiety.26 To test the model, we used four different goodness-of-fit indices: Q-statistic, the comparative fit index (CFI), the goodness-of-fit statistic (GFI), and the parsimonious normed fit index (PNFI). A Q-statistic value of 3.00 or lower, CFI and GFI values of 0.90 or higher, and a PNFI value of 0.50 or lower indicate good model fit.27
We considered all P values less than 0.05 to be statistically significant. All statistical analyses were conducted using SPSS, version 21.0, and AMOS 21.0 (SPSS Inc., Chicago, IL, USA).
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5

Examining Exercise Motivation's Impact

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In this review, IBM SPSS 26.0 and AMOS 21.0 factual programming were utilized for all information examination. After the poll was gathered, all information was handled as follows: (1) Exploratory variable investigation was led for all scales utilizing SPSS 26.0; (2) Corroborative component examination was performed for all scales utilizing AMOS 21.0; (3) SPSS 26.0 was utilized to test the inner consistency of all scales; (4) “Harman single element strategy” was utilized for the normal technique deviation test; and (5) Pearson connection investigation with SPSS 26.0 was utilized to compute the connection between exercise motivation, exercise climate, exercise self-efficacy, and exercise behavior. The mean (M) and standard deviation (SD) of persistent factors with a typical dissemination were utilized. (6) The SPSS full-scale program ordered by Hayes in SPSS 26.0 was utilized to confirm the intervening job of exercise climate and exercise self-efficacy in the connection between exercise motivation and exercise behavior and the chain intervening job of exercise climate and exercise self-efficacy in the connection between exercise motivation and exercise behavior; (7) Model 6 in the SPSS full-scale program aggregated by Hayes was utilized for the chain intervention test. In this review, the significance level was set at p < 0.05.
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6

Nurses' Intention to Use Mobile Nursing App

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SPSS 19.0 (SPSS; IBM, Armonk, NY, USA) and AMOS 21.0 (SPSS; IBM, Armonk, NY, USA) were used for analyses. Firstly, descriptive analysis was performed to describe characteristics of participants by using SPSS 19.0. Secondly, in measurement model assessment, reliability and validity of items were determined using Cronbach alpha (α), standardized factor loading, composite reliability (CR) and average variance extracted (AVE) to check for both reliability and validity of constructs by using SPSS 19.0 and AMOS 21.0. Thirdly, AMOS 21.0 was applied to build a structural equation model for influencing factors of nurses’ behavioral intention to use a mobile nursing app. There were 8 latent variables and 24 observed variables in this structural equation model. AMOS 21.0 was further applied to test the structural equation model fitting and hypothesis test of influencing factors of nurses’ behavioral intention to use a mobile nursing app.
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7

Online Learning Platform Design and Evaluation

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The reliability and validity of the questionnaire are tested using Statistical Product and Service Solutions (SPSS) 22.0. Analysis of the questionnaire is analyzed using Analysis of Moment Structure (AMOS) 21.0 and SPSS 22.0 software. After checking the fit of the SEM, AMOS 21.0 software is used to import and analyze the 210 collected data. The path analysis results and fitting results of SPE for college students are obtained. The online course design here is carried out using the Attention, Relevance, Confidence, Satisfaction (ARCS) model.
The development environment of the learning platform is shown in Table 1.
Table 1 shows that the structured query language (MySQL) is adopted as the database management software. In Table 1, the final Entity Relationship Diagram (ERD) is obtained by analyzing the relationship between entities in the online learning platform [32 (link)]. The entities included in this learning platform system are cognitive diagnosis results, cognitive diagnosis answers, students, cognitive diagnosis questionnaires, personalized recommendation resource libraries, curriculum learning resource libraries, forums, minority sensitive words, posts, works, forum replies, and learning behavior.
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8

Entrepreneurial Intention: Non-Cognitive and Social Factors

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This study applied Amos21.0 (IBM, New York, NY, USA) to test and analyze the internal mechanism of the impact of non-cognitive ability and social support perception on entrepreneurial intention. The structural equation model consists of three latent variables, namely non-cognitive ability, social support perception, and entrepreneurial intention, each of which was measured by multiple items in the scale. In order to further verify the mediating effect and influence mechanism of social support perception, the bootstrap method was used.
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9

Descriptive Statistics and CFA Analysis

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Descriptive statistics of the study population were obtained in SPSS 20.0 (IBM). Mean (M) values are reported with standard deviations (SDs). CFA, including modification index determination, SEM, and goodness of fit index calculations, was completed in Amos 21.0 (IBM).
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10

Validation of Chinese TPAS Scale

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Data analyses were performed using IBM SPSS 26.0 and Amos 21.0. Item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), correlation analysis, and reliability analysis were used. First, we performed item analysis using the independent samples t test and item-total correlation to assess the quality of items. After that, EFA was performed to explore the factor structure and CFA was performed to verify the factor structure. The goodness-of-fit of the CFA models was evaluated by chi-square/degree of freedom ratio (χ2/df), comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and root mean squared residual (SRMR). Among these indicators, the χ2/df values of up to 3 are treated as acceptable, CFI and TLI value above .90 represents reasonable fit, and RMSEA values equal and SRMR values equal to or less than 0.08 are considered as acceptable (Hu & Bentler, 1999 (link); Medsker, 1994 (link)). Third, we used Pearson product-moment correlation analysis to test the evidence based on relations to other variables. Finally, we assessed the reliability of the Chinese version of the TPAS by Cronbach’s alphas coefficient and the odd-even split-half reliabilities.
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